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Learning Deep Generative Models of Graphs

This is an implementation of Learning Deep Generative Models of Graphs by Yujia Li, Oriol Vinyals, Chris Dyer, Razvan Pascanu, Peter Battaglia.

For molecule generation, see DGL-LifeSci.

Dependencies

Usage

python3 main.py

Performance

90% accuracy for cycles compared with 84% accuracy reported in the original paper.

Speed

On AWS p3.2x instance (w/ V100), one epoch takes ~526s.

Acknowledgement

We would like to thank Yujia Li for providing details on the implementation.